On stratified bivariate ranked set sampling with optimal allocation for naïve and ratio estimators

Lili Yu, Hani Samawi, Daniel Linder, Arpita Chatterjee, Yisong Huang and Robert Vogel

Journal of Applied Statistics, 2017, vol. 44, issue 3, 457-473

Abstract: The purpose of the current work is to introduce stratified bivariate ranked set sampling (SBVRSS) and investigate its performance for estimating the population mean using both naïve and ratio methods. The properties of the proposed estimator are derived along with the optimal allocation with respect to stratification. We conduct a simulation study to demonstrate the relative efficiency of SBVRSS as compared to stratified bivariate simple random sampling (SBVSRS) for ratio estimation. Data that consist of weights and bilirubin levels in the blood of 120 babies are used to illustrate the procedure on a real data set. Based on our simulation, SBVRSS for ratio estimation is more efficient than using SBVSRS in all cases.

Date: 2017
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DOI: 10.1080/02664763.2016.1177495

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